Uraba_analisis
Ana Palacio-Castro
2023-06-01
Libraries
library(nortest)
library(ggmap)
library(ggplot2)
library(tidyverse)
library(plyr)
library(reshape2)
library(lme4)
library(vegan)
library(ggfortify)
library(ggthemes)
library(FactoMineR)
library(ggrepel)
library(PerformanceAnalytics)
# Plots
MyTheme<-theme_bw() +
theme(legend.position="top",
plot.background=element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
legend.box.background = element_rect(),
legend.title = element_blank(),
panel.background =element_rect(fill = NA,
color = "black"))#+
#guides(fill=guide_legend(nrow=2,byrow=TRUE), shape=guide_legend(nrow=3,byrow=TRUE))
# Season_fill<-scale_fill_manual(values =
# c("#2b83ba", "#abdda4",
# "#d7191c", "#fdae61"))
#
# Season_colour<-scale_colour_manual(values =
# c("#2b83ba", "#abdda4",
# "#d7191c", "#fdae61"))
#
Site_shapes13<- scale_shape_manual(values=c(0,3,2,1,16,5,6,7,4,15,8,17,18))
# Zone_shapes3<- scale_shape_manual(values=c(21,23,24))Leer/explorar datos
Data
Time points, dates, time, and sample location
Data<-read.csv("Data/All_data.csv")
Data$TimePoint<-factor(Data$TimePoint, levels = c("4M", "5M", "6M", "7M"))
Data$Station<-as.factor(Data$Station)
Data$Site<-as.factor(Data$Site)
Data$Date<-as.Date(Data$Date)
row.names(Data)<-Data$Sample
# head(Data)
summary(Data)## TimePoint Station Sample Lat Lon
## 4M:13 1 : 4 Length:51 Min. :8.005 Min. :76.51
## 5M:12 2 : 4 Class :character 1st Qu.:8.021 1st Qu.:76.74
## 6M:13 3 : 4 Mode :character Median :8.126 Median :76.85
## 7M:13 4 : 4 Mean :8.284 Mean :76.82
## 5 : 4 3rd Qu.:8.522 3rd Qu.:76.90
## 7 : 4 Max. :8.882 Max. :76.95
## (Other):27
## Site Date Time Meta_ID
## Candelaria: 4 Min. :2015-12-02 Length:51 Length:51
## Currulao : 4 1st Qu.:2016-01-11 Class :character Class :character
## El Uno : 4 Median :2016-04-25 Mode :character Mode :character
## Leoncito : 4 Mean :2016-04-05
## Margarita : 4 3rd Qu.:2016-06-24
## Marirrio : 4 Max. :2016-08-28
## (Other) :27
## Transparency_m Temperature_C Salinity_psu pH
## Min. : 0.095 Min. :27.39 Min. : 0.1573 Min. :7.435
## 1st Qu.: 0.645 1st Qu.:28.19 1st Qu.: 6.5087 1st Qu.:8.033
## Median : 1.275 Median :28.78 Median :10.7620 Median :8.195
## Mean : 2.362 Mean :28.89 Mean :14.1235 Mean :8.162
## 3rd Qu.: 2.965 3rd Qu.:29.55 3rd Qu.:23.8611 3rd Qu.:8.363
## Max. :10.050 Max. :30.61 Max. :29.7127 Max. :8.805
##
## DO_mg.L Chla_mg.m3 Seston_mg.L Speed
## Min. :2.710 Min. : 0.0000 Min. : 6.667 Min. :0.0000
## 1st Qu.:3.112 1st Qu.: 0.7983 1st Qu.: 11.150 1st Qu.:0.2729
## Median :3.360 Median : 2.2388 Median : 16.250 Median :0.5864
## Mean :3.718 Mean : 3.2988 Mean : 30.325 Mean :0.5754
## 3rd Qu.:4.230 3rd Qu.: 4.1218 3rd Qu.: 27.414 3rd Qu.:0.7751
## Max. :5.750 Max. :21.7979 Max. :191.000 Max. :1.8159
##
## Biomass Biovolumen Taxa_S Shannon_H
## Min. :0.006667 Min. : 4.554 Min. : 4.00 Min. :0.03649
## 1st Qu.:0.011475 1st Qu.: 12.413 1st Qu.: 8.00 1st Qu.:0.46165
## Median :0.016550 Median : 24.808 Median :11.00 Median :1.01400
## Mean :0.029742 Mean : 78.686 Mean :11.29 Mean :0.99827
## 3rd Qu.:0.027414 3rd Qu.: 89.548 3rd Qu.:14.00 3rd Qu.:1.41050
## Max. :0.191000 Max. :523.588 Max. :18.00 Max. :2.15400
##
## Equitability_J Acaro Bivalvo Cangrejos
## Min. :0.02632 Min. : 0.000 Min. : 0.0 Min. : 0.000
## 1st Qu.:0.23210 1st Qu.: 0.000 1st Qu.: 0.0 1st Qu.: 0.000
## Median :0.42480 Median : 0.000 Median : 0.0 Median : 0.000
## Mean :0.40791 Mean : 7.501 Mean : 2080.0 Mean : 7.511
## 3rd Qu.:0.57365 3rd Qu.: 0.000 3rd Qu.: 405.8 3rd Qu.: 0.000
## Max. :0.76010 Max. :170.257 Max. :53086.5 Max. :383.078
##
## Chaetognata Copepoda Cladocera Euphasido
## Min. : 0.00 Min. : 83 Min. : 0 Min. : 0
## 1st Qu.: 0.00 1st Qu.: 30055 1st Qu.: 0 1st Qu.: 0
## Median : 39.45 Median : 124980 Median : 0 Median : 0
## Mean : 11696.04 Mean : 584733 Mean : 20184 Mean : 2579
## 3rd Qu.: 3565.00 3rd Qu.: 238307 3rd Qu.: 1641 3rd Qu.: 735
## Max. :294204.03 Max. :12943820 Max. :378119 Max. :60861
##
## Gasteropodo Hydromedusa Huevos.redondos Huevos.ovalados
## Min. : 0.0 Min. : 0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 282.3 1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 1144.1 Median : 0 Median : 116.3 Median : 0.0
## Mean : 13913.3 Mean : 17246 Mean : 4973.3 Mean :1086.4
## 3rd Qu.: 6216.6 3rd Qu.: 2315 3rd Qu.: 1789.6 3rd Qu.: 433.2
## Max. :138024.9 Max. :333855 Max. :92634.7 Max. :9864.4
##
## Insectos Larvas.de.Peces Larvas.Brachiura Larvas.Camarón
## Min. : 0.00 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 0.00 1st Qu.: 183.1 1st Qu.: 276.4 1st Qu.: 0.0
## Median : 0.00 Median : 487.8 Median : 817.4 Median : 111.4
## Mean : 173.12 Mean : 1929.6 Mean : 14442.4 Mean : 1529.8
## 3rd Qu.: 76.47 3rd Qu.: 1359.1 3rd Qu.: 5994.6 3rd Qu.: 1538.0
## Max. :3912.36 Max. :31298.9 Max. :220273.4 Max. :13504.2
##
## Larvas.Insectos Luciféridos Myscidaceos Oikopleura
## Min. : 0.00 Min. : 0.0 Min. : 0.0 Min. : 0
## 1st Qu.: 0.00 1st Qu.: 25.5 1st Qu.: 0.0 1st Qu.: 0
## Median : 0.00 Median : 1202.3 Median : 613.3 Median : 0
## Mean : 91.61 Mean : 26043.3 Mean : 13649.5 Mean : 17862
## 3rd Qu.: 0.00 3rd Qu.: 13708.7 3rd Qu.: 5443.8 3rd Qu.: 5396
## Max. :3784.25 Max. :802368.9 Max. :291975.7 Max. :422535
##
## Ostracoda Pez.juvenil Polichaeto Porcelanidos
## Min. : 0.0 Min. : 0.00 Min. : 0.00 Min. : 0.0
## 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.0
## Median : 0.0 Median : 0.00 Median : 0.00 Median : 0.0
## Mean : 567.7 Mean : 13.91 Mean : 381.39 Mean : 117.3
## 3rd Qu.: 0.0 3rd Qu.: 0.00 3rd Qu.: 19.19 3rd Qu.: 0.0
## Max. :14921.6 Max. :368.02 Max. :5308.65 Max. :1721.7
##
## Pteropoda Stomatopoda Huevos.indeterminados Diptera.pupa
## Min. : 0 Min. : 0.00 Min. : 0.0 Min. : 0.000
## 1st Qu.: 0 1st Qu.: 95.97 1st Qu.: 0.0 1st Qu.: 0.000
## Median : 0 Median : 1717.35 Median : 0.0 Median : 0.000
## Mean : 254 Mean : 16719.64 Mean : 476.1 Mean : 4.277
## 3rd Qu.: 0 3rd Qu.: 16648.06 3rd Qu.: 0.0 3rd Qu.: 0.000
## Max. :10819 Max. :176968.24 Max. :12343.5 Max. :218.111
## Station<-Data %>% select(c("Station", "Site"))Data exploration
Data from time points 4 to 7 are used, since the multiparameter was not calibrated in the previous time points.
chart.Correlation(Data %>% select('Transparency_m':'Seston_mg.L'), method = "spearman")## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
#chart.Correlation(Data, method = "kendall")
chart.Correlation(Data %>% select('Transparency_m':'Taxa_S'), method = "spearman")## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
#chart.Correlation(Data, method = "kendall")
chart.Correlation(Data %>% select('Acaro':'Larvas.de.Peces'), method = "spearman")## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
#chart.Correlation(Data, method = "kendall")
chart.Correlation(Data %>% select('Larvas.de.Peces':'Diptera.pupa'), method = "spearman")## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
## Warning in cor.test.default(as.numeric(x), as.numeric(y), method = method):
## Cannot compute exact p-value with ties
#chart.Correlation(Data, method = "kendall")1 Physicochemical data and river-ocecean transition
1.1 correlations
head(Data) # 1. Visualizar variables
plot(Data %>% select('Transparency_m':'Seston_mg.L'))
chart.Correlation(Data[10:16])
# Transparency_m, Temperature_C, salinidad y conductividad parecen relacionarse.
# Tal vez tambien pH y temperatuora
# 2. Son estas variables normales?
Tem<-ggplot(Data, aes(x=Temperature_C)) +
geom_histogram()+ MyTheme
Tem ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#library(nortest) # Paquete que tiene varios test de normalidad cvm supuestamente sirve para muestras pequenas
cvm.test(Data$Temperature_C) # P> 0.05 No se puede rechazar la Ho de que los datos son normales :)##
## Cramer-von Mises normality test
##
## data: Data$Temperature_C
## W = 0.076161, p-value = 0.2273 qqnorm(Data$Temperature_C)
qqline(Data$Temperature_C)
Sal<-ggplot(Data, aes(x=Salinity_psu))+
geom_histogram()+ MyTheme
Sal## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
cvm.test(Data$Salinity_psu) # NO normal##
## Cramer-von Mises normality test
##
## data: Data$Salinity_psu
## W = 0.30306, p-value = 0.0002841 qqnorm(Data$Salinity_psu)
qqline(Data$Salinity_psu)
Trans<-ggplot(Data, aes(x=Transparency_m ))+
geom_histogram()+ MyTheme
Trans## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
cvm.test(Data$Transparency_m) # NO normal##
## Cramer-von Mises normality test
##
## data: Data$Transparency_m
## W = 0.70635, p-value = 5.135e-08 qqnorm(Data$Transparency_m)
qqline(Data$Transparency_m)
pH<-ggplot(Data, aes(x=pH ))+
geom_histogram()+ MyTheme
pH## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
cvm.test(Data$pH) # NO normal##
## Cramer-von Mises normality test
##
## data: Data$pH
## W = 0.13792, p-value = 0.03307 qqnorm(Data$pH)
qqline(Data$pH)
Segundo, evaluar las correlaciones. * Recuerden que en teoria no se deben calcular correlaciones con datos que no son normales (Salinidad y Temperature_C)
# Temepratura vs Transparency_m (Salinidad en color)
Temperature<-ggplot(Data, aes(x=Transparency_m, y=Temperature_C, colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+
scale_colour_gradient(low="blue", high = "red")+
xlab("Transparency (m)")+ ylab("Temperature (C)")+
geom_text_repel(aes(x=Transparency_m,
y=Temperature_C, label = Site), size=3)
Temperature
Temperature+facet_wrap(~TimePoint)
# Modelo
Temperature.lm<-lm(data = Data, Temperature_C~Transparency_m)
summary(Temperature.lm)##
## Call:
## lm(formula = Temperature_C ~ Transparency_m, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4354 -0.6869 -0.1950 0.6467 1.5537
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.51762 0.15474 184.294 < 2e-16 ***
## Transparency_m 0.15573 0.04408 3.533 0.000908 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8174 on 49 degrees of freedom
## Multiple R-squared: 0.203, Adjusted R-squared: 0.1867
## F-statistic: 12.48 on 1 and 49 DF, p-value: 0.000908# Salinidad vs Transparency_m (Temperature en color)
Salinidad<-ggplot(Data, aes(x=Transparency_m,
y=Salinity_psu, colour=Temperature_C)) +
scale_colour_gradient(low="blue", high = "red")+
geom_point()+ MyTheme+
xlab("Transparency (m)")+ ylab("Salinity (psu)")+
geom_text_repel(aes(x=Transparency_m, y=Salinity_psu,
label = Site), size=3)
Salinidad## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Salinidad +facet_wrap(~TimePoint) ## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Salinidad<-ggplot(Data, aes(x=Transparency_m,
y=Salinity_psu, colour=Temperature_C)) +
scale_colour_gradient(low="blue", high = "red")+
geom_point()+ MyTheme+
xlab("Transparency (m)")+ ylab("Salinity (psu)")+
geom_text_repel(aes(x=Transparency_m, y=Salinity_psu,
label = Site), size=3)
Salinidad## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Salinidad +facet_wrap(~TimePoint) ## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Modelo
Salinidad.lm<-lm(data = Data, Salinity_psu~Transparency_m)
summary(Salinidad.lm)##
## Call:
## lm(formula = Salinity_psu ~ Transparency_m, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.836 -5.351 -1.172 4.132 19.185
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.5064 1.3812 6.159 1.33e-07 ***
## Transparency_m 2.3779 0.3935 6.043 2.01e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.296 on 49 degrees of freedom
## Multiple R-squared: 0.427, Adjusted R-squared: 0.4153
## F-statistic: 36.52 on 1 and 49 DF, p-value: 2.009e-07 # Recuerden que salinidad no es normal
# Temperetura vs Salinidad (Transparencia en color)
Salinidad2<-ggplot(Data, aes(x=Temperature_C, y=Salinity_psu, colour=Transparency_m)) +
#geom_smooth(method=lm, colour="gray", se=FALSE)+
scale_colour_gradient(low="blue", high = "red")+
geom_point()+ MyTheme+ xlab("Temperature (C)")+ ylab("Salinity (psu)")+
geom_text_repel(aes(x=Temperature_C, y=Salinity_psu, label = Site), size=3)
Salinidad2
Salinidad2 +facet_wrap(~TimePoint) ## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Salinidad2.lm<-lm(data = Data, Salinity_psu~Temperature_C)
summary(Salinidad2.lm)##
## Call:
## lm(formula = Salinity_psu ~ Temperature_C, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.7027 -6.3585 -0.9322 6.9330 17.7539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -131.124 38.184 -3.434 0.001219 **
## Temperature_C 5.028 1.321 3.806 0.000393 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.468 on 49 degrees of freedom
## Multiple R-squared: 0.2281, Adjusted R-squared: 0.2124
## F-statistic: 14.48 on 1 and 49 DF, p-value: 0.0003934 # Yo no consideraria este, pues las dos variables NO son normales.
# pH vs Transparency_m (Salinidad en color)
pH_1<-ggplot(Data, aes(x=Transparency_m, y=pH, colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Tranparency (m)")+ ylab("pH")+
geom_text_repel(aes(x=Transparency_m, y=pH, label = Site), size=3)
pH_1
pH_1 +facet_wrap(~TimePoint) 
# Modelo
pH_1.lm<-lm(data = Data, pH~Transparency_m)
summary(pH_1.lm)##
## Call:
## lm(formula = pH ~ Transparency_m, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.60770 -0.14068 0.05923 0.15038 0.59761
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.03461 0.04307 186.545 < 2e-16 ***
## Transparency_m 0.05391 0.01227 4.394 5.96e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2275 on 49 degrees of freedom
## Multiple R-squared: 0.2826, Adjusted R-squared: 0.268
## F-statistic: 19.3 on 1 and 49 DF, p-value: 5.963e-05# pH vs Temperature (Salinidad en color)
pH_2<-ggplot(Data, aes(x=Temperature_C, y=pH, colour=Salinity_psu)) +
scale_colour_gradient(low="blue", high = "red")+
geom_point()+ MyTheme+ xlab("Temperature (m)")+ ylab("pH")+
geom_text_repel(aes(x=Temperature_C, y=pH, label = Site), size=3)
pH_2 +facet_wrap(~TimePoint) 
pH_3<-ggplot(Data, aes(x=Temperature_C, y=pH, colour=Seston_mg.L )) +
scale_colour_gradient(low="blue", high = "red")+
geom_point()+ MyTheme+ xlab("Temperature (m)")+ ylab("pH")+
geom_text_repel(aes(x=Temperature_C, y=pH, label = Site), size=3)
pH_3 +facet_wrap(~TimePoint) 
# Modelo
pH_2.lm<-lm(data = Data, pH~Temperature_C)
summary(pH_2.lm)##
## Call:
## lm(formula = pH ~ Temperature_C, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.53287 -0.18341 0.04029 0.17521 0.43052
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.11538 1.06423 3.867 0.000325 ***
## Temperature_C 0.14009 0.03683 3.804 0.000395 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.236 on 49 degrees of freedom
## Multiple R-squared: 0.228, Adjusted R-squared: 0.2122
## F-statistic: 14.47 on 1 and 49 DF, p-value: 0.0003953 # Chla vs Oxigeno (Tranparecia en color)
Chla<-ggplot(Data, aes(x=(Chla_mg.m3), y=DO_mg.L, colour=Transparency_m)) +
scale_colour_gradient(low="blue", high = "red")+
geom_point()+ MyTheme+ xlab("Chlorophyll-a")+ ylab("Dissolved Oxygen")+
geom_text_repel(aes(x=(Chla_mg.m3), y=DO_mg.L, label = Site), size=3)
Chla
Chla + facet_wrap(~TimePoint)## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Modelo
Oxigeno.lm<-lm(data = Data, DO_mg.L~Chla_mg.m3)
summary(Oxigeno.lm)##
## Call:
## lm(formula = DO_mg.L ~ Chla_mg.m3, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1014 -0.6483 -0.3260 0.5924 1.9286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.82141 0.16220 23.560 <2e-16 ***
## Chla_mg.m3 -0.03128 0.03060 -1.022 0.312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9067 on 49 degrees of freedom
## Multiple R-squared: 0.02087, Adjusted R-squared: 0.0008918
## F-statistic: 1.045 on 1 and 49 DF, p-value: 0.3118 # Chla vs Tranparecia (Oxigeno en color)
Chla_2<-ggplot(Data, aes(x=Transparency_m, y=Chla_mg.m3, colour=DO_mg.L)) +
scale_colour_gradient(low="blue", high = "red")+
geom_point()+ MyTheme+ xlab("Transparency (m)")+ ylab("Clorofila a")+
geom_text_repel(aes(x=Transparency_m, y=Chla_mg.m3, label = Site), size=3)
Chla_2## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Chla_2 + facet_wrap(~TimePoint)
# Modelo
Chla.lm<-lm(data = Data, Chla_mg.m3~Transparency_m)
summary(Chla.lm)##
## Call:
## lm(formula = Chla_mg.m3 ~ Transparency_m, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0210 -2.1736 -1.3153 0.6629 17.5930
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2642 0.7747 5.504 1.34e-06 ***
## Transparency_m -0.4087 0.2207 -1.852 0.0701 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.092 on 49 degrees of freedom
## Multiple R-squared: 0.0654, Adjusted R-squared: 0.04632
## F-statistic: 3.429 on 1 and 49 DF, p-value: 0.0701 # Recuerden que Temperature_C no es normal
# Chla vs Transparency (Seston en color)
Chla_Tranparencia<-ggplot(Data, aes(x=Transparency_m, y=Chla_mg.m3, colour=Seston_mg.L)) +
scale_colour_gradient(low="blue", high = "red")+
geom_point()+ MyTheme+ xlab("Transparency (m)")+ ylab("Clorofila a")+
geom_text_repel(aes(x=Transparency_m, y=Chla_mg.m3, label = Site), size=3)
Chla_Tranparencia## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Chla_Tranparencia + facet_wrap(~TimePoint)
# Chla vs Seston_mg.L (Transparency en color)
Chla_Seston<-ggplot(Data, aes(x=Seston_mg.L, y=Chla_mg.m3, colour=Transparency_m)) +
scale_colour_gradient(low="blue", high = "red")+
geom_point()+ MyTheme+ xlab("Seston_mg.L")+ ylab("Clorofila a")+
geom_text_repel(aes(x=Seston_mg.L, y=Chla_mg.m3, label = Site), size=3)
Chla_Seston ## Warning: ggrepel: 17 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Chla_Seston + facet_wrap(~TimePoint)## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider increasing max.overlaps
## ggrepel: 1 unlabeled data points (too many overlaps). Consider increasing max.overlaps## Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Modelo
Chla.lm<-lm(data = Data, Chla_mg.m3~Transparency_m)
summary(Chla.lm)##
## Call:
## lm(formula = Chla_mg.m3 ~ Transparency_m, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0210 -2.1736 -1.3153 0.6629 17.5930
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2642 0.7747 5.504 1.34e-06 ***
## Transparency_m -0.4087 0.2207 -1.852 0.0701 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.092 on 49 degrees of freedom
## Multiple R-squared: 0.0654, Adjusted R-squared: 0.04632
## F-statistic: 3.429 on 1 and 49 DF, p-value: 0.0701 # Salinidad vs BIOmasa
Seston<-ggplot(Data, aes(x=Salinity_psu, y=Seston_mg.L, colour=Temperature_C)) +
scale_colour_gradient(low="blue", high = "red")+
geom_point()+ MyTheme+ xlab("Salinity (psu)")+ ylab("Seston_mg.L")+
geom_text_repel(aes(x=Salinity_psu, y=Seston_mg.L, label = Site), size=3)
Seston + facet_wrap(~TimePoint)## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Modelo
Seston.lm<-lm(data = Data, Seston_mg.L~Salinity_psu)
summary(Seston.lm)##
## Call:
## lm(formula = Seston_mg.L ~ Salinity_psu, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.914 -22.130 0.119 8.345 129.191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.4024 7.8557 7.944 2.33e-10 ***
## Salinity_psu -2.2712 0.4623 -4.913 1.04e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.19 on 49 degrees of freedom
## Multiple R-squared: 0.33, Adjusted R-squared: 0.3163
## F-statistic: 24.13 on 1 and 49 DF, p-value: 1.043e-05 # Transparecia vs Seston_mg.L
Seston2<-ggplot(Data, aes(x=Transparency_m, y=Seston_mg.L, colour=Salinity_psu)) +
scale_colour_gradient(low="blue", high = "red")+
geom_point()+ MyTheme+ xlab("Transparency (m)")+ ylab("Seston_mg.L")+
geom_text_repel(aes(x=Transparency_m, y=Seston_mg.L, label = Site), size=3)
Seston2## Warning: ggrepel: 12 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Seston + facet_wrap(~TimePoint)## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Pueden correr mas variables fisicas si quieren, pero lo mejor para mostrar la correlacion entre las variables fcoquimicas (usandolas todas al mismo timpo es el PCA)
1.2 PCA of physicochimical data
Data_PCA<-Data %>% select(-c('Station':'Lon'))
Data_PCA<-Data_PCA %>% select(-c('Date':'Meta_ID'))
Data_PCA<-Data_PCA %>% select(c('TimePoint':'Seston_mg.L'))
Data.pca <- rda(Data_PCA[, (3:9)], scale=TRUE)
Data.pca2 <- prcomp(Data_PCA[, (3:9)], scale=TRUE)
summary(Data.pca)##
## Call:
## rda(X = Data_PCA[, (3:9)], scale = TRUE)
##
## Partitioning of correlations:
## Inertia Proportion
## Total 7 1
## Unconstrained 7 1
##
## Eigenvalues, and their contribution to the correlations
##
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Eigenvalue 3.1651 1.3562 0.8511 0.59344 0.47114 0.39712 0.1659
## Proportion Explained 0.4522 0.1937 0.1216 0.08478 0.06731 0.05673 0.0237
## Cumulative Proportion 0.4522 0.6459 0.7675 0.85227 0.91957 0.97630 1.0000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 4.325308
##
##
## Species scores
##
## PC1 PC2 PC3 PC4 PC5 PC6
## Transparency_m 1.28117 -0.04575 0.12608 0.54870 -0.82621 0.010475
## Temperature_C 1.11959 -0.77865 0.43462 -0.09305 0.36949 0.671515
## Salinity_psu 1.44115 0.50383 0.07725 0.15435 0.09817 0.132440
## pH 1.32672 0.14265 0.32417 0.17074 0.52394 -0.668230
## DO_mg.L -0.02617 -1.51462 0.29544 -0.21441 -0.21235 -0.376489
## Chla_mg.m3 -0.66139 0.58756 1.34339 -0.22830 -0.18108 0.005344
## Seston_mg.L -1.13537 -0.32059 0.25833 1.06077 0.27928 0.066823
##
##
## Site scores (weighted sums of species scores)
##
## PC1 PC2 PC3 PC4 PC5 PC6
## 4M1 -1.076830 -1.01587 0.53168 2.22219 0.84200 -0.855697
## 4M2 -0.358397 -1.20344 -0.19297 -0.75319 -0.34556 0.039110
## 4M3 -0.295485 -1.01365 -0.10290 -0.78322 -0.42033 -0.186251
## 4M4 -0.940537 -0.74587 -0.35471 0.47337 -0.37950 0.120977
## 4M5 -0.004642 -0.76561 0.34968 -0.72771 -0.62068 -0.041287
## 4M7 -0.102872 -1.22635 0.03881 -0.40230 0.41377 -0.546132
## 4M8 -0.341022 -0.95192 0.06621 -0.40043 0.14537 0.415916
## 4M9 -0.169264 -1.27068 -0.28084 -0.82796 -0.21110 -0.460255
## 4M10 0.486840 -1.13058 0.30952 -0.43656 0.64804 -0.222779
## 4M12 0.779011 -0.79483 0.17998 0.16561 0.04504 0.021013
## 4M13 0.733636 -0.75046 0.18401 0.05949 -0.18264 -0.447766
## 4M14 1.094638 -0.71861 0.21708 0.69710 -1.26141 0.099898
## 4M15 0.840154 -0.47988 0.50067 0.50331 -1.51235 -0.113629
## 5M1 0.126326 0.71263 -0.82402 0.03633 0.07820 -0.341584
## 5M2 -0.191983 0.42064 -0.83612 -0.22382 0.18111 -0.493608
## 5M3 -0.256547 0.23594 -0.78792 -0.11752 0.30501 -0.061759
## 5M4 -0.358857 0.38433 -0.15775 -0.47236 -0.11269 -0.137730
## 5M5 -0.191345 0.31667 -0.81889 -0.16316 -0.09993 -0.539714
## 5M7 -0.198516 0.19072 -0.59689 0.40087 0.69381 -1.409937
## 5M8 -0.377251 0.26367 -0.40683 0.05297 0.18446 -0.800225
## 5M9 -0.203897 0.38814 -0.10181 -0.34475 -0.02507 -1.120788
## 5M10 0.384583 0.42285 -0.54895 -0.03383 0.33219 -0.208362
## 5M12 0.325662 0.63874 -0.31096 -0.12009 0.32882 -0.140859
## 5M13 0.321391 0.68800 -0.44873 -0.08555 0.59046 -0.466831
## 5M15 0.068519 1.04851 1.33127 -0.48893 0.23949 -0.209179
## 6M1 -1.315602 0.83628 2.26810 0.32624 -0.47572 -0.500012
## 6M2 -0.223366 0.17081 -0.19892 -0.64931 -0.24065 0.486100
## 6M3 -0.403859 0.08002 -0.22508 -0.76769 -0.33343 0.904511
## 6M4 -1.142759 0.11138 -0.58831 0.82340 -0.25337 0.864617
## 6M5 -0.329357 0.56470 -0.22292 -0.40391 -0.23104 -0.747158
## 6M7 -0.485883 0.55123 0.92757 -0.75314 -0.17742 -0.113511
## 6M8 -1.052181 0.43474 -0.40989 -0.42913 -1.04856 0.857337
## 6M9 -0.178995 0.44291 0.76444 -0.59949 -0.03393 -0.570643
## 6M10 0.351774 0.38309 -0.43319 0.08813 -0.01852 -0.148028
## 6M12 0.571103 0.44312 -0.27806 0.30558 -0.16572 0.353877
## 6M13 0.494029 0.45744 -0.50951 0.33359 -0.17189 -0.275996
## 6M14 0.934032 0.45167 0.15386 0.92658 -1.36664 -0.094269
## 6M15 0.731613 0.52224 0.23708 0.79255 -1.52599 -0.523068
## 7M1 -1.003386 0.36135 -0.83630 0.19796 -0.56291 0.896333
## 7M2 0.308080 -0.08602 0.07206 -0.13603 0.26586 1.006411
## 7M3 -0.054616 -0.04630 0.24127 -0.27370 0.60029 -0.461106
## 7M4 -1.125772 -0.33021 -0.10947 1.80962 0.54853 0.731758
## 7M5 0.121441 -0.20153 -0.01168 -0.42201 0.70470 0.774865
## 7M7 0.369486 0.40951 -0.13464 0.10321 0.39038 1.540387
## 7M8 -0.251418 0.22418 1.69701 -0.36498 0.53556 0.900095
## 7M9 -0.016945 0.10507 -0.11962 -0.35887 0.08937 0.000496
## 7M10 0.656361 -0.08157 0.45574 0.12218 0.65790 0.761013
## 7M12 0.920577 0.11020 0.46046 0.27012 1.43681 -0.132371
## 7M13 0.478078 0.20678 -0.08751 0.07209 0.77915 0.273543
## 7M14 0.797560 0.20868 0.08272 0.40906 0.50859 0.669462
## 7M15 0.756691 0.02715 -0.13384 0.34803 0.23213 0.652816# Checking if the PC axes are meaningful
eigenval <- Data.pca$CA$eig # Here you will get the eigenvalues
sitecoord <- Data.pca$CA$u[,1:2] # The site coordinates along PC1 and PC2
eig <- data.frame(eigenval)
eig$nb <- c(1:length(eigenval))
eig$prop <- eig$eigenval/sum(eig$eigenval)
eig# (Kaiser-Guttman)
par(mfrow=c(1,2))
barplot(eig$eigenval, main="Eigenvalues",las=2)
abline(h=mean(eig$eigenval),col="red") # average eigenvalue
legend("topright","Average eigenvalue",lwd=1,col=2,bty="n")
barplot(100*eig$prop,main="% of variance",las=2)
par(mfrow=c(1,1))
autoplot(Data.pca2, data = Data_PCA, colour="TimePoint",
shape="Site",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
MyTheme + Site_shapes13 # + facet_wrap(~TimePoint)
2. Biological data
Riqueza, H, Densidad, Biomasa…
Riqueza<-Data[,c("Sample", "Site","TimePoint", "Taxa_S", "Shannon_H", "Equitability_J")]
Riqueza.data <- melt(Riqueza, id.vars = c("Sample", "Site","TimePoint"))
Riqueza.data$Site<-factor(Riqueza.data$Site,
levels=c("El Uno", "Roto", "Leoncito",
"Currulao", "Candelaria", "Yarumal",
"Marirrio", "Margarita",
"RioNecocli","P.Arenas N", "P.Arenas S",
"Bajo Medio", "Sabanilla"
))
Riqueza_grafica<-ggplot(Riqueza.data, aes(x=Site, y=value), fill=="white") +
MyTheme+geom_bar(stat="identity", aes(fill=Site))+
theme(legend.position="right", legend.box = "vertical")
Riqueza_grafica + facet_wrap(~variable, ncol=1, scales = "free_y")
Riqueza_grafica + facet_grid(~variable~TimePoint, scales = "free_y") + MyTheme
Riqueza_grafica2<-ggplot(Riqueza.data, aes(x=TimePoint, y=value), fill=="white") +
theme_bw()+geom_bar(stat="identity", aes(fill=TimePoint))+
theme(legend.position="right", legend.box = "vertical")
#Riqueza_grafica2 + facet_wrap(~variable, ncol=1, scales = "free_y")
Riqueza_grafica2 + facet_grid(~variable~Site, scales = "free_y") + MyTheme
Densidad por taxa
Abundancias<-Data %>% select('Acaro':'Diptera.pupa')
Abundancias$Sample<-Data$Sample
Abundancias$Site<-Data$Site
Abundancias$TimePoint<-Data$TimePoint
Abundancia.data <- melt(Abundancias, id.vars = c("Sample", "Site", "TimePoint"))
colnames(Abundancia.data)<-c("Sample", "Site", "TimePoint", "Taxon", "Densidad")
Abundancia.data$Densidad<-(Abundancia.data$Densidad/1000)
Abundancia.data$Site<-factor(Abundancia.data$Site,
levels=c("El Uno", "Roto", "Leoncito",
"Currulao", "Candelaria", "Yarumal",
"Marirrio", "Margarita",
"RioNecocli","P.Arenas N", "P.Arenas S",
"Bajo Medio", "Sabanilla"
))Densidad - muestreo
Densidad<-ggplot(Abundancia.data, aes(x=Taxon, y=Densidad, fill=TimePoint)) +
MyTheme + geom_bar(stat="identity")+
xlab("Taxa")+ ylab("Densidad (#/m3)")+ scale_y_continuous(trans='sqrt')+
facet_wrap(~Site)
Densidad
Densidad<-ggplot(Abundancia.data, aes(x=Taxon, y=(log10(1+Densidad)),
fill=TimePoint)) +
MyTheme + geom_bar(stat="identity")+
theme(legend.position="right", legend.box = "vertical",
#axis.text.x = element_blank(),
axis.title.x = element_blank())+
xlab("Taxa")+ ylab("Densidad de organismos(log10 (#/m3 + 1))")+
facet_wrap(~Site, ncol=3)
Densidad
Densidad - muestreo
Densidad<-ggplot(Abundancia.data, aes(x=Site, y=Densidad, fill=Taxon)) +
MyTheme + geom_bar(stat="identity")+
xlab("Taxa")+ ylab("Densidad (#/m3)")+ scale_y_continuous(trans='sqrt')+
facet_wrap(~TimePoint)
Densidad
Densidad<-ggplot(Abundancia.data, aes(x=Site, y=(log10(1+Densidad)),
fill=Taxon)) +
MyTheme + geom_bar(stat="identity")+
theme(legend.position="bottom", legend.box = "vertical",
#axis.text.x = element_blank(),
axis.title.x = element_blank())+
xlab("Taxa")+ ylab("Densidad de organismos(log10 (#/m3 + 1))")+
facet_wrap(~TimePoint, ncol=4)
Densidad
Explorar relacion entre fcoqcos y biologicos
Huevos redondos
Hu.re_1<-ggplot(Data, aes(x=Chla_mg.m3, y=Huevos.redondos, colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Clorofila")+ ylab("Huevos Redondos")+
geom_text_repel(aes(x=Chla_mg.m3, y=Huevos.redondos, label = Site), size=3)
Hu.re_1## Warning: ggrepel: 38 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Hu.re_1+ facet_wrap(~TimePoint)## Warning: ggrepel: 11 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 9 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Hu.re_2<-ggplot(Data, aes(x=Salinity_psu, y=Huevos.redondos,
colour=Transparency_m)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Salinity (psu)")+ ylab("Huevos Redondos")+
geom_text_repel(aes(x=Salinity_psu, y=Huevos.redondos, label = Site), size=3)
Hu.re_2## Warning: ggrepel: 30 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Hu.re_2 + facet_wrap(~TimePoint)## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Hu.re_3<-ggplot(Data, aes(x=Transparency_m, y=Huevos.redondos,
colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Transparency_m")+ ylab("Huevos Redondos")+
geom_text_repel(aes(x=Transparency_m, y=Huevos.redondos, label = Site), size=3)
Hu.re_3## Warning: ggrepel: 34 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Hu.re_3 + facet_wrap(~TimePoint)## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 12 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Huevos ovalados
Hu.ov_1<-ggplot(Data, aes(x=Chla_mg.m3, y=Huevos.ovalados, colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Clorofila")+ ylab("Huevos Ovalados")+
geom_text_repel(aes(x=Chla_mg.m3, y=Huevos.ovalados, label = Site), size=3)
Hu.ov_1## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Hu.ov_1+facet_wrap(~TimePoint)## Warning: ggrepel: 8 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 8 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Hu.ov_2<-ggplot(Data, aes(x=Salinity_psu, y=Huevos.ovalados,
colour=Transparency_m)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Salinity psu")+ ylab("Huevos Ovalados")+
geom_text_repel(aes(x=Salinity_psu, y=Huevos.ovalados, label = Site), size=3)
Hu.ov_2 + facet_wrap(~TimePoint)## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Esta me parece que tiene futuro...
Hu.ov_3<-ggplot(Data, aes(x=Transparency_m, y=Huevos.ovalados,
colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Transparency_m")+ ylab("Huevos Ovalados")+
geom_text_repel(aes(x=Transparency_m, y=Huevos.ovalados, label = Site), size=3)
Hu.ov_3 + facet_wrap(~TimePoint)## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Esta me parece tambien puede tener futuro... Organismos
Org_1<-ggplot(Data, aes(x=Chla_mg.m3, y=Seston_mg.L , colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Clorofila")+ ylab("Organismos")+
geom_text_repel(aes(x=Chla_mg.m3, y=Seston_mg.L , label = Site), size=3)
Org_1 + facet_wrap(~TimePoint)## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 9 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Org_2<-ggplot(Data, aes(x=Salinity_psu, y=Seston_mg.L , colour=Transparency_m)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Salinity_psu")+ ylab("Organismos")+
geom_text_repel(aes(x=Salinity_psu, y=Seston_mg.L , label = Site), size=3)
Org_2 + facet_wrap(~TimePoint)## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Org_3<-ggplot(Data, aes(x=Transparency_m, y=Seston_mg.L , colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Transparency_m")+ ylab("Organismos")+
geom_text_repel(aes(x=Transparency_m, y=Seston_mg.L,
label = Site), size=3)
Org_3 + facet_wrap(~TimePoint)## Warning: ggrepel: 8 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Biomasa volumetrica
Vol_1<-ggplot(Data, aes(x=Chla_mg.m3, y= Biovolumen , colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Clorofila")+ ylab("Biovolumen")+
geom_text_repel(aes(x=Chla_mg.m3, y= Biovolumen , label = Site), size=3)
Vol_1## Warning: ggrepel: 24 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Vol_1 + facet_wrap(~TimePoint)## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
BioVol_Sal<-ggplot(Data, aes(x=Salinity_psu, y= Biovolumen , colour=Chla_mg.m3)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Salinity_psu (psu)")+ ylab("Biovolumen")+
geom_text_repel(aes(x=Salinity_psu, y= Biovolumen , label = Site), size=3)
BioVol_Sal + facet_wrap(~TimePoint)
Vol_2<-ggplot(Data, aes(x=Salinity_psu, y= Biovolumen , colour=Transparency_m)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Salinity_psu")+ ylab("Biomasa")+
geom_text_repel(aes(x=Salinity_psu, y= Biovolumen , label = Site), size=3)
Vol_2 + facet_wrap(~TimePoint)
Vol_3<-ggplot(Data, aes(x=Transparency_m, y= Biovolumen , colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Transparency_m")+ ylab("Biomasa")+
geom_text_repel(aes(x=Transparency_m, y= Biovolumen , label = Site), size=3)
Vol_3 + facet_wrap(~TimePoint)## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Seston
Seston_1<-ggplot(Data, aes(x=Chla_mg.m3, y=Seston_mg.L, colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Clorofila")+ ylab("Seston")+
geom_text_repel(aes(x=Chla_mg.m3, y=Seston_mg.L, label = Site), size=3)
Seston_1## Warning: ggrepel: 27 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Seston_1+ facet_wrap(~TimePoint)## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 9 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Seston_2<-ggplot(Data, aes(x=Salinity_psu, y=Seston_mg.L, colour=Transparency_m)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Salinity_psu")+ ylab("Seston")+
geom_text_repel(aes(x=Salinity_psu, y=Seston_mg.L, label = Site), size=3)
Seston_2 + facet_wrap(~TimePoint)## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Seston_3<-ggplot(Data, aes(x=Transparency_m, y=Seston_mg.L,
colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Transparency_m")+ ylab("Seston")+
geom_text_repel(aes(x=Transparency_m, y=Seston_mg.L, label = Site), size=3)
Seston_3 + facet_wrap(~TimePoint)## Warning: ggrepel: 8 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Chl_3<-ggplot(Data, aes(x=Transparency_m, y=Chla_mg.m3 , colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Transparency_m")+ ylab("Clorophila a")+
geom_text_repel(aes(x=Transparency_m, y=Chla_mg.m3, label = Site), size=3)
Chl_3## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Chl_3 + facet_wrap(~TimePoint)
Riqueza
S_1<-ggplot(Data, aes(x=Chla_mg.m3, y=Taxa_S , colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Clorofila")+ ylab("Riqueza")+
geom_text_repel(aes(x=Chla_mg.m3, y=Taxa_S , label = Site), size=3)
S_1
S_1 + facet_wrap(~TimePoint)
# S_1b<-ggplot(Data, aes(x=Salinity_psu, y=Taxa_S , colour=Chla_mg.m3)) +
# #geom_smooth(method=lm, colour="gray")+
# geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
# xlab("Salinidad (psu)")+ ylab("Riqueza")+
# geom_text_repel(aes(x=Salinity_psu, y=Taxa_S , label = Site), size=3)
# S_1b
# S_1 + facet_wrap(~TimePoint)
S_2<-ggplot(Data, aes(x=Salinity_psu, y=Taxa_S , colour=Transparency_m)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Salinidad (psu)")+ ylab("Riqueza")+
geom_text_repel(aes(x=Salinity_psu, y=Taxa_S , label = Site), size=3)
S_2 + facet_wrap(~TimePoint)
S_2b<-ggplot(Data, aes(x=Salinity_psu, y=Taxa_S , colour=Transparency_m)) +
geom_smooth(method=lm, colour="gray", se=F)+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Salinidad (psu)")+ ylab("Riqueza")+
geom_text_repel(aes(x=Salinity_psu, y=Taxa_S , label = Site), size=3)
S_2b + facet_wrap(~TimePoint)## `geom_smooth()` using formula 'y ~ x'
# Modelo
Riqueza_2.lm<-lm(data = Data, Taxa_S~Salinity_psu)
summary(Riqueza_2.lm)##
## Call:
## lm(formula = Taxa_S ~ Salinity_psu, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2528 -1.6579 0.5733 1.5897 4.9652
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.27423 0.65736 11.066 6.28e-15 ***
## Salinity_psu 0.28462 0.03869 7.357 1.86e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.61 on 49 degrees of freedom
## Multiple R-squared: 0.5249, Adjusted R-squared: 0.5152
## F-statistic: 54.13 on 1 and 49 DF, p-value: 1.859e-09 # ESTE VALE LA PENA
S_3<-ggplot(Data, aes(x=Transparency_m, y=Taxa_S , colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Transparency_m")+ ylab("Riqueza")+
geom_text_repel(aes(x=Transparency_m, y=Taxa_S , label = Site), size=3)
S_3 + facet_wrap(~TimePoint)
Riqueza_3.lm<-lm(data = Data, Taxa_S~Transparency_m)
summary(Riqueza_3.lm)##
## Call:
## lm(formula = Taxa_S ~ Transparency_m, data = Data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2005 -2.2938 -0.2326 2.7983 6.1645
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.7766 0.6404 15.266 < 2e-16 ***
## Transparency_m 0.6424 0.1824 3.521 0.00094 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.383 on 49 degrees of freedom
## Multiple R-squared: 0.2019, Adjusted R-squared: 0.1856
## F-statistic: 12.4 on 1 and 49 DF, p-value: 0.0009403Diversidad
H_1<-ggplot(Data, aes(x=Chla_mg.m3, y=Shannon_H, colour=Salinity_psu)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Clorofila")+ ylab("Diversidad (H)")+
geom_text_repel(aes(x=Chla_mg.m3, y=Shannon_H, label = Site), size=3)
H_1
H_1+ facet_wrap(~TimePoint)
H_1b<-ggplot(Data, aes(x=Salinity_psu, y=Shannon_H , colour=Chla_mg.m3)) +
#geom_smooth(method=lm, colour="gray")+
geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
xlab("Salinidad (psu)")+ ylab("Diversidad")+
geom_text_repel(aes(x=Salinity_psu, y=Shannon_H , label = Site), size=3)
H_1b
H_1b + facet_wrap(~TimePoint)
que era Densidad ???
# Densidad_1<-ggplot(Data, aes(x=Chla_mg.m3, y=Densidad , colour=Salinity_psu)) +
# #geom_smooth(method=lm, colour="gray")+
# geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
# xlab("Clorofila")+ ylab("Densidad")+
# geom_text_repel(aes(x=Chla_mg.m3, y=Densidad , label = Site), size=3)
# Densidad_1
# Densidad_1 + facet_wrap(~TimePoint)
# tiff('Densidad_Chla_2.tiff', units="in", width=5, height=4, res=300)
# Densidad_1 + xlim(0,0.005)
# dev.off()
#
# Densidad_2<-ggplot(Data, aes(x=Salinity_psu, y=Densidad , colour=Chla_mg.m3)) +
# #geom_smooth(method=lm, colour="gray")+
# geom_point()+ MyTheme+ scale_colour_gradient(low="blue", high = "red")+
# xlab("Salinidad (psu)")+ ylab("Densidad")+
# geom_text_repel(aes(x=Salinity_psu, y=Densidad , label = Site), size=3)
# Densidad_2 + facet_wrap(~TimePoint)
#
# tiff('Densidad_Salinidad.tiff', units="in", width=5, height=4, res=300)
# Densidad_2
# dev.off()PCA con datos biologicos
- Hay muchos datos, decidir se se usan todos los grupos
#library(vegan)
# 1.Select only abundance data
Abundancias2<-Abundancias
str(Abundancias2)## 'data.frame': 51 obs. of 30 variables:
## $ Acaro : num 170.3 45.5 0 54.5 0 ...
## $ Bivalvo : num 0 0 0 0 0 ...
## $ Cangrejos : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Chaetognata : num 0 0 0 0 33510 ...
## $ Copepoda : num 83937 29280 133463 30481 121007 ...
## $ Cladocera : num 681 19262 1181 709 0 ...
## $ Euphasido : num 4937 0 8858 22411 0 ...
## $ Gasteropodo : num 0 911 13583 0 7447 ...
## $ Hydromedusa : num 0 0 0 0 20478 ...
## $ Huevos.redondos : num 0 0 0 0 116 ...
## $ Huevos.ovalados : num 0 0 0 0 0 ...
## $ Insectos : num 170.3 227.7 0 54.5 0 ...
## $ Larvas.de.Peces : num 426 6694 31299 872 15533 ...
## $ Larvas.Brachiura : num 0 364 220273 382 94944 ...
## $ Larvas.Camarón : num 0 45.5 7086.5 0 3723.3 ...
## $ Larvas.Insectos : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Luciféridos : num 0 0 39567 709 802369 ...
## $ Myscidaceos : num 0 0 36614 491 96806 ...
## $ Oikopleura : num 0 0 0 0 0 ...
## $ Ostracoda : num 3235 0 0 0 0 ...
## $ Pez.juvenil : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Polichaeto : num 0 0 0 0 0 ...
## $ Porcelanidos : num 0 0 0 0 0 ...
## $ Pteropoda : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Stomatopoda : num 0 0 18897 327 100529 ...
## $ Huevos.indeterminados: num 0 0 0 0 0 0 0 0 0 0 ...
## $ Diptera.pupa : num 0 0 0 218 0 ...
## $ Sample : chr "4M1" "4M2" "4M3" "4M4" ...
## $ Site : Factor w/ 13 levels "Bajo Medio","Candelaria",..: 11 2 6 5 7 3 4 13 10 9 ...
## $ TimePoint : Factor w/ 4 levels "4M","5M","6M",..: 1 1 1 1 1 1 1 1 1 1 ...row.names(Abundancias2)<-Abundancias2$Sample
bio2<-Abundancias2 %>% select(-c("Sample":"TimePoint"))
# 1. Transformar los datos de abundancia (hay varias opciones)
? decostand
bio.transf.1 = decostand(bio2,"total") # relative abundance profiles
bio.transf.2 = decostand(bio2,"norm") # chord-transformation
bio.transf.3 = decostand(bio2,"chi.sq") # chi-square transformation
bio.transf.4 = decostand(bio2,"hel") # Hellinger transformation
# "The Hellinger distance is also a measure recommended for clustering or
# ordination of species abundance data (Rao 1995)".
bio.transf.5 = decostand(bio2,"pa") # convert to presence/absence (0/1)
Bio1.pca <- rda(bio.transf.1, scale=TRUE) # 'scale=TRUE' calls for a standardization
Bio2.pca <- rda(bio.transf.2, scale=TRUE) # 'scale=TRUE' calls for a standardization
Bio3.pca <- rda(bio.transf.3, scale=TRUE) # 'scale=TRUE' calls for a standardization
Bio4.pca <- rda(bio.transf.4, scale=TRUE) # 'scale=TRUE' calls for a standardization
Bio5.pca <- rda(bio.transf.5, scale=TRUE) # 'scale=TRUE' calls for a standardization
# Pueden revisar los PCAs con cada transformacion. A mi me parece que #4 es la mejor para separar los sitios
summary(Bio1.pca)##
## Call:
## rda(X = bio.transf.1, scale = TRUE)
##
## Partitioning of correlations:
## Inertia Proportion
## Total 27 1
## Unconstrained 27 1
##
## Eigenvalues, and their contribution to the correlations
##
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Eigenvalue 3.725 2.58907 2.29786 2.15797 1.81000 1.7360 1.44892
## Proportion Explained 0.138 0.09589 0.08511 0.07992 0.06704 0.0643 0.05366
## Cumulative Proportion 0.138 0.23385 0.31896 0.39889 0.46592 0.5302 0.58388
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Eigenvalue 1.40092 1.34170 1.16433 0.98638 0.94540 0.83000 0.75504
## Proportion Explained 0.05189 0.04969 0.04312 0.03653 0.03501 0.03074 0.02796
## Cumulative Proportion 0.63577 0.68546 0.72858 0.76512 0.80013 0.83087 0.85884
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Eigenvalue 0.70691 0.55369 0.52934 0.48189 0.37492 0.33998 0.31500
## Proportion Explained 0.02618 0.02051 0.01961 0.01785 0.01389 0.01259 0.01167
## Cumulative Proportion 0.88502 0.90552 0.92513 0.94298 0.95686 0.96946 0.98112
## PC22 PC23 PC24 PC25 PC26
## Eigenvalue 0.199414 0.134127 0.083006 0.060833 0.032313
## Proportion Explained 0.007386 0.004968 0.003074 0.002253 0.001197
## Cumulative Proportion 0.988508 0.993476 0.996550 0.998803 1.000000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 6.061547
##
##
## Species scores
##
## PC1 PC2 PC3 PC4 PC5
## Acaro -0.377823 0.250156 -0.128305 0.2180336 -0.038233
## Bivalvo 0.321105 -0.111036 0.360234 0.0307207 0.141574
## Cangrejos 0.349753 -0.214214 0.523387 0.5258613 0.381232
## Chaetognata 0.626730 -0.315503 0.370275 0.5413594 0.344216
## Copepoda -0.567052 -0.040946 0.383538 -0.4173598 -0.406665
## Cladocera -0.284361 0.221652 -0.184916 0.5782361 -0.171864
## Euphasido -0.450050 0.377432 -0.320584 0.8488221 -0.057241
## Gasteropodo 0.729960 0.020059 0.560539 0.3308249 -0.098372
## Hydromedusa 0.049702 0.016266 0.097250 -0.0726936 0.494089
## Huevos.redondos 0.612391 0.003271 -0.674951 -0.0400936 -0.055364
## Huevos.ovalados 0.333355 -0.119446 0.194927 -0.0004376 -0.691715
## Insectos 0.008929 -0.154568 -0.451801 -0.1640890 -0.081642
## Larvas.de.Peces -0.317922 0.036288 -0.057742 -0.2048338 0.270896
## Larvas.Brachiura -0.028067 -0.066806 -0.053588 -0.2049102 0.074796
## Larvas.Camarón 0.588799 -0.039413 0.202700 -0.0949015 -0.528696
## Larvas.Insectos -0.274533 0.221089 -0.175676 0.6193031 -0.092307
## Luciféridos 0.439658 -0.387803 -0.640210 -0.0439502 0.008689
## Myscidaceos 0.493123 -0.486007 -0.489011 0.0401227 0.150649
## Oikopleura 0.435900 -0.192160 0.165505 0.2935276 -0.243432
## Ostracoda 0.520868 0.946383 -0.090451 -0.1789286 0.085331
## Pez.juvenil 0.016818 -0.166913 0.226817 0.0139464 0.278273
## Polichaeto 0.706008 0.733403 0.002029 -0.0956090 -0.012572
## Porcelanidos 0.544310 -0.438474 -0.622910 0.1131419 0.037236
## Pteropoda 0.586933 0.931084 -0.086275 -0.1742046 0.100296
## Stomatopoda -0.034105 0.020299 0.053838 -0.2435730 0.584843
## Huevos.indeterminados 0.185059 -0.128978 0.070965 0.0430459 -0.590688
## Diptera.pupa -0.291588 0.231305 -0.216179 0.4841210 -0.035383
## PC6
## Acaro 0.2854207
## Bivalvo -0.6673047
## Cangrejos 0.3871988
## Chaetognata 0.3987689
## Copepoda 0.6066198
## Cladocera -0.2811747
## Euphasido -0.1394629
## Gasteropodo -0.0036053
## Hydromedusa -0.5403846
## Huevos.redondos 0.0953366
## Huevos.ovalados -0.3615121
## Insectos 0.1416232
## Larvas.de.Peces -0.1464670
## Larvas.Brachiura -0.0765993
## Larvas.Camarón -0.2067702
## Larvas.Insectos -0.2497093
## Luciféridos -0.0639155
## Myscidaceos 0.0411154
## Oikopleura -0.0005565
## Ostracoda 0.1817465
## Pez.juvenil 0.2262665
## Polichaeto 0.0271835
## Porcelanidos 0.2157604
## Pteropoda 0.1280080
## Stomatopoda -0.4922368
## Huevos.indeterminados -0.2461140
## Diptera.pupa 0.1390456
##
##
## Site scores (weighted sums of species scores)
##
## PC1 PC2 PC3 PC4 PC5 PC6
## 4M1 -0.967890 0.735277 -0.14981 0.10682 -0.260753 1.304965
## 4M2 -0.925760 0.379210 -0.27989 0.16082 0.162623 0.004709
## 4M3 0.190997 -0.332839 -0.20531 -0.54553 -0.014151 -0.595411
## 4M4 -1.500210 1.190054 -1.11223 2.49078 -0.182044 0.715384
## 4M5 0.459471 -0.795121 -1.25424 -0.17273 0.527235 -0.427100
## 4M7 -0.603525 0.011602 0.27746 -0.54348 -0.132953 0.495520
## 4M8 -0.594797 0.006591 0.28154 -0.48700 -0.234080 0.628747
## 4M9 -0.633594 0.086286 0.24551 -0.39785 -0.307825 0.754121
## 4M10 1.008136 -0.304507 1.32051 0.82330 0.087503 0.263075
## 4M12 1.352302 -1.003379 -1.43298 0.27335 -0.189399 0.641432
## 4M13 1.799465 -1.102122 2.69280 2.70553 1.961421 1.992121
## 4M14 -0.519364 0.011249 0.31241 -0.40315 -0.308634 0.544084
## 4M15 -0.238449 -0.050461 0.45097 -0.43098 -1.134149 0.057551
## 5M1 0.786123 -0.029766 0.90368 0.17725 -0.856929 -0.104596
## 5M2 -0.085369 -0.116593 0.52061 0.06907 0.049263 0.280422
## 5M3 0.010987 -0.411732 -0.21385 -0.47018 0.353906 -0.144541
## 5M4 -0.557815 -0.002363 0.27564 -0.51323 -0.183320 0.502546
## 5M5 -0.447912 0.025980 0.26186 -0.37988 -0.232755 0.369049
## 5M7 -0.479989 -0.222009 0.61624 -0.50818 0.594245 0.601312
## 5M8 0.164000 0.809176 0.23131 -0.40078 0.521043 -0.414803
## 5M9 -0.182263 0.053101 0.05190 -0.23368 1.512758 -1.554201
## 5M10 1.364449 -1.634353 -1.54175 0.66024 0.943994 0.868413
## 5M12 -0.438254 -0.042368 0.19126 -0.44130 0.095567 0.343258
## 5M13 -0.006253 -0.545947 0.05249 -0.27704 0.731895 0.349677
## 5M15 -0.600366 0.175164 0.17815 0.35895 -0.348103 0.026890
## 6M1 -0.370999 0.645693 0.01482 -0.26129 -0.056956 0.427407
## 6M2 -0.394200 0.033600 0.10745 -0.52139 1.100396 -0.727323
## 6M3 -0.470715 0.003922 -0.05240 -0.87098 1.427050 -1.273428
## 6M4 -0.705916 0.149341 0.04353 -0.24010 0.065349 0.138219
## 6M5 -0.641309 -0.255754 -0.69484 -0.94932 -0.241394 0.801825
## 6M7 -0.528656 -0.033171 0.21797 -0.50392 -0.240227 0.571400
## 6M8 -0.587524 0.002337 0.27658 -0.49488 -0.222467 0.613623
## 6M9 -0.454427 0.049572 0.18961 -0.53300 0.814812 -0.497957
## 6M10 1.360460 -1.198720 -3.59616 -0.20395 0.265327 0.385382
## 6M12 3.026333 4.788042 -0.44623 -0.89699 0.518407 0.656551
## 6M13 0.345722 -0.270239 0.68493 -0.17985 1.895856 -3.102875
## 6M14 0.251645 -0.296566 0.31975 -0.12509 0.058990 -0.429062
## 6M15 0.746686 -0.022903 0.04861 0.87109 -0.597583 -0.227708
## 7M1 -1.263405 1.203117 -1.16293 3.41769 -0.347825 -1.495096
## 7M2 -0.018601 -0.252504 0.44161 -0.10973 -0.270911 0.364051
## 7M3 -0.181199 0.045699 0.25994 0.11486 0.934408 -0.749394
## 7M4 -0.725506 0.533132 -0.51654 1.37807 -0.200017 -0.787461
## 7M5 -0.361398 -0.038338 0.34478 -0.54959 -0.622367 0.471656
## 7M7 0.490499 -0.202212 -0.87324 -0.14473 0.009788 0.472934
## 7M8 -0.380810 -0.085298 -0.06004 -0.59997 0.276493 -0.080883
## 7M9 -0.332225 -0.196957 0.04853 -0.45683 -0.191058 0.468791
## 7M10 1.502442 -0.262031 0.71877 0.06901 -2.141754 -1.707899
## 7M12 0.989281 -0.606456 0.36768 0.25204 -2.909714 -1.323932
## 7M13 -0.026228 -0.285073 0.03261 -0.08622 -0.822200 0.065067
## 7M14 -0.513516 0.136550 0.09528 0.10586 -0.390211 0.014347
## 7M15 0.889449 -0.474914 0.51565 -0.10193 -1.268549 -0.550858 summary(Bio4.pca) # By default scaling=2##
## Call:
## rda(X = bio.transf.4, scale = TRUE)
##
## Partitioning of correlations:
## Inertia Proportion
## Total 27 1
## Unconstrained 27 1
##
## Eigenvalues, and their contribution to the correlations
##
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Eigenvalue 5.3894 2.3491 2.29256 2.19579 1.89438 1.53292 1.50042
## Proportion Explained 0.1996 0.0870 0.08491 0.08133 0.07016 0.05677 0.05557
## Cumulative Proportion 0.1996 0.2866 0.37152 0.45284 0.52301 0.57978 0.63535
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Eigenvalue 1.3420 1.21263 0.98898 0.88339 0.80163 0.71480 0.57409
## Proportion Explained 0.0497 0.04491 0.03663 0.03272 0.02969 0.02647 0.02126
## Cumulative Proportion 0.6851 0.72997 0.76660 0.79932 0.82901 0.85548 0.87674
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Eigenvalue 0.52901 0.47325 0.42238 0.40444 0.34109 0.265801 0.229612
## Proportion Explained 0.01959 0.01753 0.01564 0.01498 0.01263 0.009844 0.008504
## Cumulative Proportion 0.89634 0.91386 0.92951 0.94449 0.95712 0.966963 0.975467
## PC22 PC23 PC24 PC25 PC26 PC27
## Eigenvalue 0.215050 0.159161 0.116764 0.089340 0.056736 0.0253394
## Proportion Explained 0.007965 0.005895 0.004325 0.003309 0.002101 0.0009385
## Cumulative Proportion 0.983432 0.989327 0.993651 0.996960 0.999062 1.0000000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 6.061547
##
##
## Species scores
##
## PC1 PC2 PC3 PC4 PC5 PC6
## Acaro 0.458944 -0.38528 0.04749 -0.02110 0.11640 -0.594979
## Bivalvo -0.542575 0.30729 -0.30491 -0.26218 0.27002 0.015305
## Cangrejos -0.340245 0.25889 -0.17164 -0.31658 0.44238 -0.263699
## Chaetognata -0.888876 0.09212 -0.06118 -0.17055 0.35899 -0.168514
## Copepoda 0.449557 0.29977 0.17644 -0.55146 -0.58943 -0.095526
## Cladocera 0.261429 -0.51836 0.26492 -0.41590 0.31993 0.142023
## Euphasido 0.554082 -0.52050 -0.03101 -0.13752 0.65523 -0.231099
## Gasteropodo -0.854735 0.16717 -0.23970 -0.43745 0.06814 -0.300387
## Hydromedusa -0.197888 0.25892 -0.43525 0.30654 0.37148 0.424128
## Huevos.redondos -0.786295 -0.52170 0.14435 0.31145 -0.07024 0.162143
## Huevos.ovalados -0.558643 -0.05961 0.44284 -0.50400 -0.18278 0.059871
## Insectos 0.062212 -0.35967 0.45692 0.35317 -0.14221 -0.082414
## Larvas.de.Peces 0.509628 0.15070 -0.09578 0.20883 -0.11237 -0.533302
## Larvas.Brachiura 0.008559 0.24636 -0.13953 0.32947 -0.22085 -0.295844
## Larvas.Camarón -0.716605 0.07734 0.18353 -0.14361 -0.42352 -0.376693
## Larvas.Insectos 0.213566 -0.23091 -0.01854 -0.26948 0.52177 0.429801
## Luciféridos -0.691587 -0.04945 0.34693 0.59891 0.13252 -0.110356
## Myscidaceos -0.691315 0.05698 0.27024 0.53416 0.13867 -0.102242
## Oikopleura -0.751473 0.01235 0.21392 -0.43329 0.14575 -0.149788
## Ostracoda -0.167351 -0.70667 -0.69414 0.01375 -0.35521 -0.067896
## Pez.juvenil -0.123434 0.49672 -0.21306 -0.01975 0.23265 -0.206481
## Polichaeto -0.692121 -0.38340 -0.48527 -0.27425 -0.13444 -0.008244
## Porcelanidos -0.707101 -0.26841 0.40337 0.33645 0.10120 0.070329
## Pteropoda -0.333262 -0.59625 -0.77448 0.05483 -0.33325 -0.008516
## Stomatopoda -0.090896 0.36325 -0.40704 0.45496 0.12654 -0.146216
## Huevos.indeterminados -0.277800 -0.14613 0.46553 -0.23105 -0.13690 -0.005153
## Diptera.pupa 0.288307 -0.35529 0.08378 0.05461 0.40138 -0.653467
##
##
## Site scores (weighted sums of species scores)
##
## PC1 PC2 PC3 PC4 PC5 PC6
## 4M1 1.17838 -1.289432 -0.157966 -0.26795 -0.25764 -0.999340
## 4M2 1.04318 -0.658509 0.409821 -0.24109 -0.18332 -1.261174
## 4M3 0.01768 0.617858 0.006277 1.13769 -0.54416 -1.525139
## 4M4 1.48333 -1.827968 0.431051 0.28099 2.06510 -3.362059
## 4M5 -0.41005 0.453180 0.209142 1.83099 0.37624 -0.351330
## 4M7 0.66839 0.456528 0.031390 -0.24782 -0.79623 0.002527
## 4M8 0.79142 0.243195 0.060475 -0.30064 -0.55192 0.656903
## 4M9 0.73299 0.009065 0.186808 -0.28142 -0.65864 -0.267910
## 4M10 -1.01531 0.707305 -0.402881 -0.65375 0.38198 -0.480762
## 4M12 -1.49625 -0.620810 1.063744 0.31123 0.03887 0.115199
## 4M13 -1.75055 1.331962 -0.883087 -1.62881 2.27602 -1.356720
## 4M14 0.46462 0.146631 0.237557 -0.69976 -0.55423 0.602880
## 4M15 0.01529 -0.009006 0.461421 -1.05137 -0.80168 0.685449
## 5M1 -1.05619 0.308653 -0.091138 -1.20155 -0.27125 -0.448469
## 5M2 -0.14836 0.699020 -0.554249 -0.71971 0.78191 0.068754
## 5M3 -0.07994 0.928406 -0.033191 1.18043 -0.28880 -0.479202
## 5M4 0.59528 0.479224 0.020260 -0.18700 -0.76576 -0.017989
## 5M5 0.49625 0.312997 -0.119252 -0.10368 -0.53336 -0.198010
## 5M7 0.45247 1.376475 -0.688212 0.05989 -0.15521 -0.287122
## 5M8 0.02880 0.151852 -1.230981 -0.21708 0.25826 0.781719
## 5M9 0.34772 0.342670 -0.749489 1.08153 0.86839 1.745064
## 5M10 -1.38155 -0.204915 1.061815 1.58214 0.70821 0.121017
## 5M12 0.21883 0.306808 -0.183682 0.28009 -0.24031 0.617645
## 5M13 -0.32691 1.222034 -0.260275 0.63399 0.44921 -0.458854
## 5M15 0.27524 0.033207 -0.176119 -0.81466 0.53384 1.392298
## 6M1 0.76772 -0.885790 -0.617151 -0.28801 -0.46061 0.221976
## 6M2 0.49218 0.485436 -0.487807 0.60750 0.18152 -0.202741
## 6M3 0.39090 0.809335 -0.388487 1.21046 -0.32891 -1.064613
## 6M4 0.98755 -0.304907 -0.062946 -0.21625 -0.09508 -0.102449
## 6M5 0.87280 -0.198154 0.803794 0.47290 -1.09907 -0.221437
## 6M7 0.52763 0.292771 0.255021 0.09156 -0.70459 0.267209
## 6M8 0.75979 0.345745 0.013997 -0.19823 -0.64880 0.622227
## 6M9 0.61533 0.592919 -0.572462 0.29178 0.03680 0.141101
## 6M10 -0.99056 -1.502262 1.581791 2.93236 0.64680 1.159870
## 6M12 -1.76100 -3.102351 -3.968803 0.33870 -1.75278 -0.215616
## 6M13 -0.43023 1.100543 -1.331798 0.91832 1.52695 1.241899
## 6M14 -0.63787 0.394361 -0.206114 -0.46076 0.17577 0.358346
## 6M15 -0.78865 -0.915419 0.187390 -0.53442 0.18185 -0.017436
## 7M1 1.21349 -1.699287 0.155411 -0.94641 2.77278 1.718701
## 7M2 -0.46727 0.449186 0.359572 -0.62088 -0.14484 0.001720
## 7M3 0.13061 0.422839 -0.534421 0.03059 0.94596 -0.856354
## 7M4 0.95471 -1.037875 0.268370 -0.54788 1.08994 0.831446
## 7M5 0.39463 0.179551 0.453929 -0.23065 -0.96655 0.217255
## 7M7 -0.81265 -0.594573 0.196474 0.68130 -0.29475 0.282830
## 7M8 0.55569 0.501238 -0.253579 0.55712 -0.64372 0.007145
## 7M9 0.25032 0.365462 0.301795 0.26785 -0.55801 0.336129
## 7M10 -1.70157 -0.300864 0.765660 -1.12789 -0.35692 -0.291172
## 7M12 -1.40381 -0.636372 2.020057 -1.07818 -0.54888 -0.150497
## 7M13 -0.37453 -0.392077 1.262998 -0.48237 -0.45776 0.230937
## 7M14 0.43692 -0.174485 0.422807 -0.89794 -0.25504 0.640723
## 7M15 -1.12689 0.288601 0.725263 -0.53323 -0.37757 -0.452572 # This means that by default, the correlation among the descriptors is conserved
# For a scale that conserves the euclidian distance among objects you have:
summary(Bio4.pca, scaling=1) # Using scaling=1##
## Call:
## rda(X = bio.transf.4, scale = TRUE)
##
## Partitioning of correlations:
## Inertia Proportion
## Total 27 1
## Unconstrained 27 1
##
## Eigenvalues, and their contribution to the correlations
##
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Eigenvalue 5.3894 2.3491 2.29256 2.19579 1.89438 1.53292 1.50042
## Proportion Explained 0.1996 0.0870 0.08491 0.08133 0.07016 0.05677 0.05557
## Cumulative Proportion 0.1996 0.2866 0.37152 0.45284 0.52301 0.57978 0.63535
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Eigenvalue 1.3420 1.21263 0.98898 0.88339 0.80163 0.71480 0.57409
## Proportion Explained 0.0497 0.04491 0.03663 0.03272 0.02969 0.02647 0.02126
## Cumulative Proportion 0.6851 0.72997 0.76660 0.79932 0.82901 0.85548 0.87674
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Eigenvalue 0.52901 0.47325 0.42238 0.40444 0.34109 0.265801 0.229612
## Proportion Explained 0.01959 0.01753 0.01564 0.01498 0.01263 0.009844 0.008504
## Cumulative Proportion 0.89634 0.91386 0.92951 0.94449 0.95712 0.966963 0.975467
## PC22 PC23 PC24 PC25 PC26 PC27
## Eigenvalue 0.215050 0.159161 0.116764 0.089340 0.056736 0.0253394
## Proportion Explained 0.007965 0.005895 0.004325 0.003309 0.002101 0.0009385
## Cumulative Proportion 0.983432 0.989327 0.993651 0.996960 0.999062 1.0000000
##
## Scaling 1 for species and site scores
## * Sites are scaled proportional to eigenvalues
## * Species are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 6.061547
##
##
## Species scores
##
## PC1 PC2 PC3 PC4 PC5 PC6
## Acaro 1.02724 -1.30620 0.16299 -0.07400 0.4394 -2.49703
## Bivalvo -1.21443 1.04181 -1.04638 -0.91935 1.0194 0.06423
## Cangrejos -0.76156 0.87770 -0.58904 -1.11013 1.6701 -1.10670
## Chaetognata -1.98955 0.31230 -0.20994 -0.59804 1.3553 -0.70723
## Copepoda 1.00623 1.01629 0.60552 -1.93373 -2.2252 -0.40091
## Cladocera 0.58515 -1.75738 0.90914 -1.45841 1.2078 0.59605
## Euphasido 1.24019 -1.76464 -0.10643 -0.48224 2.4737 -0.96989
## Gasteropodo -1.91313 0.56674 -0.82260 -1.53398 0.2573 -1.26067
## Hydromedusa -0.44293 0.87781 -1.49370 1.07492 1.4024 1.77999
## Huevos.redondos -1.75994 -1.76870 0.49537 1.09214 -0.2652 0.68049
## Huevos.ovalados -1.25039 -0.20210 1.51975 -1.76731 -0.6900 0.25127
## Insectos 0.13925 -1.21937 1.56804 1.23843 -0.5369 -0.34588
## Larvas.de.Peces 1.14068 0.51092 -0.32871 0.73228 -0.4242 -2.23818
## Larvas.Brachiura 0.01916 0.83522 -0.47883 1.15532 -0.8338 -1.24161
## Larvas.Camarón -1.60396 0.26220 0.62984 -0.50359 -1.5989 -1.58092
## Larvas.Insectos 0.47802 -0.78286 -0.06361 -0.94497 1.9698 1.80381
## Luciféridos -1.54796 -0.16767 1.19058 2.10015 0.5003 -0.46314
## Myscidaceos -1.54735 0.19316 0.92740 1.87310 0.5235 -0.42909
## Oikopleura -1.68200 0.04188 0.73412 -1.51937 0.5502 -0.62864
## Ostracoda -0.37458 -2.39580 -2.38213 0.04821 -1.3410 -0.28495
## Pez.juvenil -0.27628 1.68401 -0.73118 -0.06927 0.8783 -0.86657
## Polichaeto -1.54915 -1.29982 -1.66535 -0.96168 -0.5076 -0.03460
## Porcelanidos -1.58268 -0.90997 1.38428 1.17981 0.3821 0.29516
## Pteropoda -0.74593 -2.02146 -2.65784 0.19225 -1.2581 -0.03574
## Stomatopoda -0.20345 1.23150 -1.39686 1.59538 0.4777 -0.61364
## Huevos.indeterminados -0.62179 -0.49541 1.59761 -0.81021 -0.5168 -0.02163
## Diptera.pupa 0.64531 -1.20454 0.28752 0.19151 1.5153 -2.74249
##
##
## Site scores (weighted sums of species scores)
##
## PC1 PC2 PC3 PC4 PC5 PC6
## 4M1 0.526467 -0.380333 -0.046030 -0.076413 -0.068244 -0.2381174
## 4M2 0.466064 -0.194235 0.119419 -0.068754 -0.048557 -0.3005058
## 4M3 0.007900 0.182244 0.001829 0.324443 -0.144138 -0.3634020
## 4M4 0.662712 -0.539180 0.125605 0.080132 0.547007 -0.8010935
## 4M5 -0.183199 0.133671 0.060943 0.522155 0.099660 -0.0837130
## 4M7 0.298617 0.134658 0.009147 -0.070672 -0.210906 0.0006020
## 4M8 0.353585 0.071733 0.017622 -0.085735 -0.146194 0.1565233
## 4M9 0.327479 0.002674 0.054435 -0.080253 -0.174462 -0.0638361
## 4M10 -0.453615 0.208628 -0.117397 -0.186434 0.101179 -0.1145535
## 4M12 -0.668485 -0.183115 0.309967 0.088754 0.010296 0.0274490
## 4M13 -0.782098 0.392877 -0.257325 -0.464497 0.602875 -0.3232719
## 4M14 0.207581 0.043250 0.069222 -0.199556 -0.146806 0.1436509
## 4M15 0.006833 -0.002656 0.134455 -0.299825 -0.212350 0.1633250
## 5M1 -0.471877 0.091041 -0.026557 -0.342653 -0.071850 -0.1068588
## 5M2 -0.066284 0.206184 -0.161504 -0.205244 0.207114 0.0163822
## 5M3 -0.035716 0.273844 -0.009672 0.336630 -0.076498 -0.1141818
## 5M4 0.265957 0.141353 0.005904 -0.053327 -0.202837 -0.0042862
## 5M5 0.221712 0.092322 -0.034749 -0.029568 -0.141277 -0.0471807
## 5M7 0.202152 0.406007 -0.200540 0.017079 -0.041112 -0.0684139
## 5M8 0.012866 0.044791 -0.358699 -0.061906 0.068408 0.1862638
## 5M9 0.155354 0.101074 -0.218396 0.308427 0.230020 0.4158046
## 5M10 -0.617238 -0.060442 0.309405 0.451189 0.187592 0.0288352
## 5M12 0.097770 0.090496 -0.053524 0.079875 -0.063653 0.1471690
## 5M13 -0.146055 0.360453 -0.075842 0.180800 0.118987 -0.1093333
## 5M15 0.122972 0.009795 -0.051320 -0.232321 0.141405 0.3317494
## 6M1 0.342997 -0.261274 -0.179833 -0.082133 -0.122008 0.0528912
## 6M2 0.219892 0.143185 -0.142143 0.173244 0.048081 -0.0483080
## 6M3 0.174642 0.238723 -0.113202 0.345194 -0.087123 -0.2536703
## 6M4 0.441210 -0.089936 -0.018342 -0.061670 -0.025186 -0.0244110
## 6M5 0.389943 -0.058448 0.234220 0.134860 -0.291122 -0.0527629
## 6M7 0.235733 0.086356 0.074311 0.026110 -0.186632 0.0636691
## 6M8 0.339453 0.101982 0.004079 -0.056530 -0.171854 0.1482610
## 6M9 0.274912 0.174888 -0.166811 0.083209 0.009747 0.0336208
## 6M10 -0.442554 -0.443109 0.460923 0.836240 0.171325 0.2763677
## 6M12 -0.786766 -0.915074 -1.156481 0.096590 -0.464280 -0.0513759
## 6M13 -0.192215 0.324618 -0.388076 0.261882 0.404460 0.2959131
## 6M14 -0.284983 0.116321 -0.060060 -0.131399 0.046557 0.0853848
## 6M15 -0.352347 -0.270013 0.054604 -0.152403 0.048168 -0.0041547
## 7M1 0.542157 -0.501224 0.045286 -0.269895 0.734459 0.4095230
## 7M2 -0.208764 0.132493 0.104777 -0.177061 -0.038366 0.0004099
## 7M3 0.058352 0.124721 -0.155727 0.008725 0.250567 -0.2040475
## 7M4 0.426537 -0.306133 0.078201 -0.156243 0.288706 0.1981125
## 7M5 0.176312 0.052961 0.132272 -0.065777 -0.256021 0.0517664
## 7M7 -0.363072 -0.175376 0.057251 0.194292 -0.078073 0.0673912
## 7M8 0.248268 0.147846 -0.073891 0.158876 -0.170509 0.0017024
## 7M9 0.111835 0.107797 0.087941 0.076384 -0.147805 0.0800910
## 7M10 -0.760219 -0.088743 0.223108 -0.321648 -0.094540 -0.0693788
## 7M12 -0.627184 -0.187705 0.588630 -0.307473 -0.145388 -0.0358596
## 7M13 -0.167330 -0.115648 0.368028 -0.137561 -0.121252 0.0550263
## 7M14 0.195206 -0.051466 0.123203 -0.256073 -0.067556 0.1526680
## 7M15 -0.503467 0.085126 0.211336 -0.152065 -0.100012 -0.1078364 eigenval <- Bio4.pca$CA$eig # Here you will get the eigenvalues
sitecoord <- Bio4.pca$CA$u[,1:2] # The site coordinates along PC1 and PC2
# Plotting the results of the PCA
#####################################
# With scaling 1:
biplot(Bio4.pca, scaling=1,main="PCA - Scaling 1")
# With scaling 2 by default
biplot(Bio4.pca,main="PCA - Scaling 2") # by default: scaling type 2
# With scaling 3 by default
biplot(Bio4.pca, scaling=3, main="PCA - Scaling 3") # by default: scaling type 2
Bio4.pca1 <- rda(bio.transf.4, scale=TRUE)
Bio4.pca <- prcomp(bio.transf.4, scale=TRUE)
summary(Bio4.pca1)##
## Call:
## rda(X = bio.transf.4, scale = TRUE)
##
## Partitioning of correlations:
## Inertia Proportion
## Total 27 1
## Unconstrained 27 1
##
## Eigenvalues, and their contribution to the correlations
##
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Eigenvalue 5.3894 2.3491 2.29256 2.19579 1.89438 1.53292 1.50042
## Proportion Explained 0.1996 0.0870 0.08491 0.08133 0.07016 0.05677 0.05557
## Cumulative Proportion 0.1996 0.2866 0.37152 0.45284 0.52301 0.57978 0.63535
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Eigenvalue 1.3420 1.21263 0.98898 0.88339 0.80163 0.71480 0.57409
## Proportion Explained 0.0497 0.04491 0.03663 0.03272 0.02969 0.02647 0.02126
## Cumulative Proportion 0.6851 0.72997 0.76660 0.79932 0.82901 0.85548 0.87674
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Eigenvalue 0.52901 0.47325 0.42238 0.40444 0.34109 0.265801 0.229612
## Proportion Explained 0.01959 0.01753 0.01564 0.01498 0.01263 0.009844 0.008504
## Cumulative Proportion 0.89634 0.91386 0.92951 0.94449 0.95712 0.966963 0.975467
## PC22 PC23 PC24 PC25 PC26 PC27
## Eigenvalue 0.215050 0.159161 0.116764 0.089340 0.056736 0.0253394
## Proportion Explained 0.007965 0.005895 0.004325 0.003309 0.002101 0.0009385
## Cumulative Proportion 0.983432 0.989327 0.993651 0.996960 0.999062 1.0000000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 6.061547
##
##
## Species scores
##
## PC1 PC2 PC3 PC4 PC5 PC6
## Acaro 0.458944 -0.38528 0.04749 -0.02110 0.11640 -0.594979
## Bivalvo -0.542575 0.30729 -0.30491 -0.26218 0.27002 0.015305
## Cangrejos -0.340245 0.25889 -0.17164 -0.31658 0.44238 -0.263699
## Chaetognata -0.888876 0.09212 -0.06118 -0.17055 0.35899 -0.168514
## Copepoda 0.449557 0.29977 0.17644 -0.55146 -0.58943 -0.095526
## Cladocera 0.261429 -0.51836 0.26492 -0.41590 0.31993 0.142023
## Euphasido 0.554082 -0.52050 -0.03101 -0.13752 0.65523 -0.231099
## Gasteropodo -0.854735 0.16717 -0.23970 -0.43745 0.06814 -0.300387
## Hydromedusa -0.197888 0.25892 -0.43525 0.30654 0.37148 0.424128
## Huevos.redondos -0.786295 -0.52170 0.14435 0.31145 -0.07024 0.162143
## Huevos.ovalados -0.558643 -0.05961 0.44284 -0.50400 -0.18278 0.059871
## Insectos 0.062212 -0.35967 0.45692 0.35317 -0.14221 -0.082414
## Larvas.de.Peces 0.509628 0.15070 -0.09578 0.20883 -0.11237 -0.533302
## Larvas.Brachiura 0.008559 0.24636 -0.13953 0.32947 -0.22085 -0.295844
## Larvas.Camarón -0.716605 0.07734 0.18353 -0.14361 -0.42352 -0.376693
## Larvas.Insectos 0.213566 -0.23091 -0.01854 -0.26948 0.52177 0.429801
## Luciféridos -0.691587 -0.04945 0.34693 0.59891 0.13252 -0.110356
## Myscidaceos -0.691315 0.05698 0.27024 0.53416 0.13867 -0.102242
## Oikopleura -0.751473 0.01235 0.21392 -0.43329 0.14575 -0.149788
## Ostracoda -0.167351 -0.70667 -0.69414 0.01375 -0.35521 -0.067896
## Pez.juvenil -0.123434 0.49672 -0.21306 -0.01975 0.23265 -0.206481
## Polichaeto -0.692121 -0.38340 -0.48527 -0.27425 -0.13444 -0.008244
## Porcelanidos -0.707101 -0.26841 0.40337 0.33645 0.10120 0.070329
## Pteropoda -0.333262 -0.59625 -0.77448 0.05483 -0.33325 -0.008516
## Stomatopoda -0.090896 0.36325 -0.40704 0.45496 0.12654 -0.146216
## Huevos.indeterminados -0.277800 -0.14613 0.46553 -0.23105 -0.13690 -0.005153
## Diptera.pupa 0.288307 -0.35529 0.08378 0.05461 0.40138 -0.653467
##
##
## Site scores (weighted sums of species scores)
##
## PC1 PC2 PC3 PC4 PC5 PC6
## 4M1 1.17838 -1.289432 -0.157966 -0.26795 -0.25764 -0.999340
## 4M2 1.04318 -0.658509 0.409821 -0.24109 -0.18332 -1.261174
## 4M3 0.01768 0.617858 0.006277 1.13769 -0.54416 -1.525139
## 4M4 1.48333 -1.827968 0.431051 0.28099 2.06510 -3.362059
## 4M5 -0.41005 0.453180 0.209142 1.83099 0.37624 -0.351330
## 4M7 0.66839 0.456528 0.031390 -0.24782 -0.79623 0.002527
## 4M8 0.79142 0.243195 0.060475 -0.30064 -0.55192 0.656903
## 4M9 0.73299 0.009065 0.186808 -0.28142 -0.65864 -0.267910
## 4M10 -1.01531 0.707305 -0.402881 -0.65375 0.38198 -0.480762
## 4M12 -1.49625 -0.620810 1.063744 0.31123 0.03887 0.115199
## 4M13 -1.75055 1.331962 -0.883087 -1.62881 2.27602 -1.356720
## 4M14 0.46462 0.146631 0.237557 -0.69976 -0.55423 0.602880
## 4M15 0.01529 -0.009006 0.461421 -1.05137 -0.80168 0.685449
## 5M1 -1.05619 0.308653 -0.091138 -1.20155 -0.27125 -0.448469
## 5M2 -0.14836 0.699020 -0.554249 -0.71971 0.78191 0.068754
## 5M3 -0.07994 0.928406 -0.033191 1.18043 -0.28880 -0.479202
## 5M4 0.59528 0.479224 0.020260 -0.18700 -0.76576 -0.017989
## 5M5 0.49625 0.312997 -0.119252 -0.10368 -0.53336 -0.198010
## 5M7 0.45247 1.376475 -0.688212 0.05989 -0.15521 -0.287122
## 5M8 0.02880 0.151852 -1.230981 -0.21708 0.25826 0.781719
## 5M9 0.34772 0.342670 -0.749489 1.08153 0.86839 1.745064
## 5M10 -1.38155 -0.204915 1.061815 1.58214 0.70821 0.121017
## 5M12 0.21883 0.306808 -0.183682 0.28009 -0.24031 0.617645
## 5M13 -0.32691 1.222034 -0.260275 0.63399 0.44921 -0.458854
## 5M15 0.27524 0.033207 -0.176119 -0.81466 0.53384 1.392298
## 6M1 0.76772 -0.885790 -0.617151 -0.28801 -0.46061 0.221976
## 6M2 0.49218 0.485436 -0.487807 0.60750 0.18152 -0.202741
## 6M3 0.39090 0.809335 -0.388487 1.21046 -0.32891 -1.064613
## 6M4 0.98755 -0.304907 -0.062946 -0.21625 -0.09508 -0.102449
## 6M5 0.87280 -0.198154 0.803794 0.47290 -1.09907 -0.221437
## 6M7 0.52763 0.292771 0.255021 0.09156 -0.70459 0.267209
## 6M8 0.75979 0.345745 0.013997 -0.19823 -0.64880 0.622227
## 6M9 0.61533 0.592919 -0.572462 0.29178 0.03680 0.141101
## 6M10 -0.99056 -1.502262 1.581791 2.93236 0.64680 1.159870
## 6M12 -1.76100 -3.102351 -3.968803 0.33870 -1.75278 -0.215616
## 6M13 -0.43023 1.100543 -1.331798 0.91832 1.52695 1.241899
## 6M14 -0.63787 0.394361 -0.206114 -0.46076 0.17577 0.358346
## 6M15 -0.78865 -0.915419 0.187390 -0.53442 0.18185 -0.017436
## 7M1 1.21349 -1.699287 0.155411 -0.94641 2.77278 1.718701
## 7M2 -0.46727 0.449186 0.359572 -0.62088 -0.14484 0.001720
## 7M3 0.13061 0.422839 -0.534421 0.03059 0.94596 -0.856354
## 7M4 0.95471 -1.037875 0.268370 -0.54788 1.08994 0.831446
## 7M5 0.39463 0.179551 0.453929 -0.23065 -0.96655 0.217255
## 7M7 -0.81265 -0.594573 0.196474 0.68130 -0.29475 0.282830
## 7M8 0.55569 0.501238 -0.253579 0.55712 -0.64372 0.007145
## 7M9 0.25032 0.365462 0.301795 0.26785 -0.55801 0.336129
## 7M10 -1.70157 -0.300864 0.765660 -1.12789 -0.35692 -0.291172
## 7M12 -1.40381 -0.636372 2.020057 -1.07818 -0.54888 -0.150497
## 7M13 -0.37453 -0.392077 1.262998 -0.48237 -0.45776 0.230937
## 7M14 0.43692 -0.174485 0.422807 -0.89794 -0.25504 0.640723
## 7M15 -1.12689 0.288601 0.725263 -0.53323 -0.37757 -0.452572# Checking if the PC axes are meaningful
eigenval <- Bio4.pca1$CA$eig # Here you will get the eigenvalues
sitecoord <- Bio4.pca1$CA$u[,1:2] # The site coordinates along PC1 and PC2
eig <- data.frame(eigenval)
eig$nb <- c(1:length(eigenval))
eig$prop <- eig$eigenval/sum(eig$eigenval)
eig# (Kaiser-Guttman)
par(mfrow=c(1,2))
barplot(eig$eigenval, main="Eigenvalues",las=2)
abline(h=mean(eig$eigenval),col="red") # average eigenvalue
legend("topright","Average eigenvalue",lwd=1,col=2,bty="n")
barplot(100*eig$prop,main="% of variance",las=2)
par(mfrow=c(1,1))
autoplot(Bio4.pca, data = Abundancias2, colour="Site",
shape="Site",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
MyTheme + Site_shapes13 ## Too few points to calculate an ellipse
autoplot(Bio4.pca, data = Abundancias2, colour="TimePoint",
shape="Site",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
MyTheme + Site_shapes13 
Podemos intentar graficar los parametros Datas con los biologicos. * Para eso hay correr el PCA con otro paquete * Este PCA puede verse super bacano, pero necesita que removamos algunas de las variables
# #library(FactoMineR)
#
# str(Bio)
# ExtraBio<-Bio[, 24:28]
# str(Data.mean)
# str(Coordinates)
# str(bio.transf.4)
#
# Bio.data<-cbind(bio.transf.4, ExtraBio, Data.mean)
# str(Bio.data)
#
# biologico.PCA <- PCA(Bio.data, quanti.sup =21:34)
#
# # Results
# biologico.PCA
#
# # You could then look at the eigenvalues, coordinates of sites and descriptors, ...
# biologico.PCA$eig
# biologico.PCA$ind$coord
# biologico.PCA$var$coord
#
# Note that the supplementary/additional descriptors are now in blue in the plot
# An other one is that it can handle missing data (see PCA help/documentation)Clasificacion con datos biologicos
- Use bio2 Data (Solo datos de abundancia)
head(bio.transf.4, n=2)d <- dist(bio.transf.4, method="euclidian")
# methods:
# euclidean
# maximum
# manhattan
# canberra
# binary
# minkowski
tW <- hclust(d, method="ward.D")
# method:
# ward.D: synoptic, best after a CA, applicable here
# ward.D2:
# single: descriptive
# complete: synoptic but less compact than ward
# average
# mcquitty
# median
# centroid
plot(tW, hang=-1)
# -> very compact groups <- effect of Ward's method. To be read only at the level of large groups
# cut three large groups
rect.hclust(tW, k=3)
# get the cluster numbers
clusters <- cutree(tW, k=3)
tS <- hclust(d, method="single")
plot(tS, hang=-1)
rect.hclust(tS, k=3)
# -> the change of aggregation method changed the structure of the tree. But this tree is not meant to look at general tendencies. It is meant to examine precise links between objects
# METODO COMPLETO
#tiff('Cluster_Bco.tiff', units="in", width=7, height=6, res=200)
tC <- hclust(d, method="complete")
plot(tC, hang=-1, xlab = "", ylab = "", main = "")
plot(tC, xlab = "", ylab = "", main = "")
rect.hclust(tC, k=3)
#dev.off()Mapas
# Atrato
Golfo <- c(left = -77.5, bottom = 7.6, right = -76.3, top = 9)
GolfoMap<-get_stamenmap(Golfo, zoom = 10, maptype = "toner-lite")## ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL. GolfoMap2<-ggmap(GolfoMap)
# library(ggrepel)
AtratoPublication<-GolfoMap2 + geom_point(data=Data,
aes(x=Lon, y=Lat), alpha=0.8, size=2) +
geom_text_repel(data=Data, aes(x=Lon, y=Lat, label = Site), size=3)
AtratoPublication## Warning: Removed 51 rows containing missing values (geom_point).## Warning: Removed 51 rows containing missing values (geom_text_repel).
# Colombia
Colombia <- c(left = -79.3, bottom = -0, right = -66.8, top = 13)
ColombiaMap<-get_stamenmap(Colombia, zoom = 5, maptype = "toner-lite")## ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL. ColombiaMap2<-ggmap(ColombiaMap)
ColombiaMap2
# Golfo con variables
Riqueza_map <- GolfoMap2 + geom_point(data=Data, aes(x=Data$Lon, y=Data$Lat,
colour=Biovolumen, size=Taxa_S))+
scale_colour_gradient(low="blue", high = "red")+
geom_text_repel(data=Data, aes(x=Lon, y=Lat, label = Site), size=3)
Riqueza_map## Warning: Use of `Data$Lon` is discouraged. Use `Lon` instead.## Warning: Use of `Data$Lat` is discouraged. Use `Lat` instead.## Warning: Removed 51 rows containing missing values (geom_point).## Warning: Removed 51 rows containing missing values (geom_text_repel).
Densidad_map<-GolfoMap2 + geom_point(data=Data, aes(x=Data$Lon, y=Data$Lat,
colour=Taxa_S, size=Biovolumen))+
scale_colour_gradient(low="blue", high = "red")+
geom_text_repel(data=Data, aes(x=Lon, y=Lat, label = Site), size=3)
Densidad_map## Warning: Use of `Data$Lon` is discouraged. Use `Lon` instead.## Warning: Use of `Data$Lat` is discouraged. Use `Lat` instead.## Warning: Removed 51 rows containing missing values (geom_point).## Warning: Removed 51 rows containing missing values (geom_text_repel).
# Copepoda_map1<-GolfoMap2 + geom_point(data=Data, aes(x=Data$lon, y=Data$lat,
# colour=Copepoda/1000, size=Densidad))+
# scale_colour_gradient(low="blue", high = "red")+
# geom_text_repel(data=Data, aes(x=lon, y=lat, label = Site), size=3)
#
# Copepoda_map2<-GolfoMap2 + geom_point(data=Data, aes(x=Data$lon, y=Data$lat,
# colour=Densidad, size=Copepoda/1000))+
# scale_colour_gradient(low="blue", high = "red")+
# geom_text_repel(data=Data, aes(x=lon, y=lat, label = Site), size=3)
#
# Copepoda_map3<-GolfoMap2 + geom_point(data=Data, aes(x=Data$lon, y=Data$lat,
# colour=Taxa_S, size=Copepoda/1000))+
# scale_colour_gradient(low="blue", high = "red")+
# geom_text_repel(data=Data, aes(x=lon, y=lat, label = Site), size=3)Packages used
# Creates bibliography
#knitr::write_bib(c(.packages()), "packages.bib")
Arnold, Jeffrey B. 2021. Ggthemes: Extra Themes, Scales and Geoms
for Ggplot2. https://github.com/jrnold/ggthemes.
Bates, Douglas, Martin Mächler, Ben Bolker, and Steve Walker. 2015.
“Fitting Linear Mixed-Effects Models Using lme4.” Journal of Statistical
Software 67 (1): 1–48. https://doi.org/10.18637/jss.v067.i01.
Bates, Douglas, Martin Maechler, Ben Bolker, and Steven Walker. 2022.
Lme4: Linear Mixed-Effects Models Using Eigen and S4. https://github.com/lme4/lme4/.
Bates, Douglas, Martin Maechler, and Mikael Jagan. 2022. Matrix:
Sparse and Dense Matrix Classes and Methods. https://CRAN.R-project.org/package=Matrix.
Gross, Juergen, and Uwe Ligges. 2015. Nortest: Tests for
Normality. https://CRAN.R-project.org/package=nortest.
Henry, Lionel, and Hadley Wickham. 2020. Purrr: Functional
Programming Tools. https://CRAN.R-project.org/package=purrr.
Horikoshi, Masaaki, and Yuan Tang. 2018. Ggfortify: Data
Visualization Tools for Statistical Analysis Results. https://CRAN.R-project.org/package=ggfortify.
———. 2022. Ggfortify: Data Visualization Tools for Statistical
Analysis Results. https://github.com/sinhrks/ggfortify.
Husson, Francois, Julie Josse, Sebastien Le, and Jeremy Mazet. 2023.
FactoMineR: Multivariate Exploratory Data Analysis and Data
Mining. http://factominer.free.fr.
Kahle, David, and Hadley Wickham. 2013. “Ggmap: Spatial
Visualization with Ggplot2.” The R Journal 5 (1):
144–61. https://journal.r-project.org/archive/2013-1/kahle-wickham.pdf.
Kahle, David, Hadley Wickham, and Scott Jackson. 2023. Ggmap:
Spatial Visualization with Ggplot2. https://github.com/dkahle/ggmap.
Lê, Sébastien, Julie Josse, and François Husson. 2008.
“FactoMineR: A Package for Multivariate
Analysis.” Journal of Statistical Software 25 (1): 1–18.
https://doi.org/10.18637/jss.v025.i01.
Müller, Kirill, and Hadley Wickham. 2022. Tibble: Simple Data
Frames. https://CRAN.R-project.org/package=tibble.
Oksanen, Jari, Gavin L. Simpson, F. Guillaume Blanchet, Roeland Kindt,
Pierre Legendre, Peter R. Minchin, R. B. O’Hara, et al. 2022. Vegan:
Community Ecology Package. https://github.com/vegandevs/vegan.
Peterson, Brian G., and Peter Carl. 2020. PerformanceAnalytics:
Econometric Tools for Performance and Risk Analysis. https://github.com/braverock/PerformanceAnalytics.
R Core Team. 2021. R: A Language and Environment for Statistical
Computing. Vienna, Austria: R Foundation for Statistical Computing.
https://www.R-project.org/.
Ryan, Jeffrey A., and Joshua M. Ulrich. 2023. Xts: eXtensible Time
Series. https://github.com/joshuaulrich/xts.
Sarkar, Deepayan. 2008. Lattice: Multivariate Data Visualization
with r. New York: Springer. http://lmdvr.r-forge.r-project.org.
———. 2021. Lattice: Trellis Graphics for r. http://lattice.r-forge.r-project.org/.
Simpson, Gavin L. 2022. Permute: Functions for Generating Restricted
Permutations of Data. https://github.com/gavinsimpson/permute.
Slowikowski, Kamil. 2021. Ggrepel: Automatically Position
Non-Overlapping Text Labels with Ggplot2. https://github.com/slowkow/ggrepel.
Tang, Yuan, Masaaki Horikoshi, and Wenxuan Li. 2016. “Ggfortify:
Unified Interface to Visualize Statistical Result of Popular r
Packages.” The R Journal 8 (2): 474–85. https://doi.org/10.32614/RJ-2016-060.
Wickham, Hadley. 2007. “Reshaping Data with the reshape Package.” Journal of
Statistical Software 21 (12): 1–20. http://www.jstatsoft.org/v21/i12/.
———. 2011. “The Split-Apply-Combine Strategy for Data
Analysis.” Journal of Statistical Software 40 (1): 1–29.
https://www.jstatsoft.org/v40/i01/.
———. 2016. Ggplot2: Elegant Graphics for Data Analysis.
Springer-Verlag New York. https://ggplot2.tidyverse.org.
———. 2020. Reshape2: Flexibly Reshape Data: A Reboot of the Reshape
Package. https://github.com/hadley/reshape.
———. 2022a. Forcats: Tools for Working with Categorical Variables
(Factors). https://CRAN.R-project.org/package=forcats.
———. 2022b. Plyr: Tools for Splitting, Applying and Combining
Data. https://CRAN.R-project.org/package=plyr.
———. 2022c. Stringr: Simple, Consistent Wrappers for Common String
Operations. https://CRAN.R-project.org/package=stringr.
———. 2022d. Tidyverse: Easily Install and Load the Tidyverse.
https://CRAN.R-project.org/package=tidyverse.
Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy
D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019.
“Welcome to the tidyverse.”
Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.
Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen,
Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey
Dunnington. 2022. Ggplot2: Create Elegant Data Visualisations Using
the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.
Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2022.
Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.
Wickham, Hadley, and Maximilian Girlich. 2022. Tidyr: Tidy Messy
Data. https://CRAN.R-project.org/package=tidyr.
Wickham, Hadley, Jim Hester, and Jennifer Bryan. 2022. Readr: Read
Rectangular Text Data. https://CRAN.R-project.org/package=readr.
Zeileis, Achim, and Gabor Grothendieck. 2005. “Zoo: S3
Infrastructure for Regular and Irregular Time Series.”
Journal of Statistical Software 14 (6): 1–27. https://doi.org/10.18637/jss.v014.i06.
Zeileis, Achim, Gabor Grothendieck, and Jeffrey A. Ryan. 2022. Zoo:
S3 Infrastructure for Regular and Irregular Time Series (z’s Ordered
Observations). https://zoo.R-Forge.R-project.org/.